How far away are truly hyperparameter-free learning algorithms?

George E. Dahl George Edward Dahl https://www.cs.toronto.edu/~gdahl/ https://scholar.google.com/citations?user=ghbWy-0AAAAJ https://openreview.net/profile?id=~George_E._Dahl1

Priya Kasimbeg Vincent Roulet Naman Agarwal Sourabh Medapati Fabian Pedregosa Atish Agarwala George E. Dahl How far away are truly hyperparameter-free learning algorithms? 2025 2025 Trans. Mach. Learn. Res. https://openreview.net/forum?id=6BlOCx5c5T db/journals/tmlr/tmlr2025.html#KasimbegRAMPAD25

Priya Kasimbeg Frank Schneider 0001 Runa Eschenhagen Juhan Bae Chandramouli Shama Sastry Mark Saroufim Boyuan Feng Less Wright Edward Z. Yang Zachary Nado Sourabh Medapati Philipp Hennig Michael Rabbat George E. Dahl Accelerating neural network training: An analysis of the AlgoPerf competition. 2025 ICLR https://openreview.net/forum?id=CtM5xjRSfm conf/iclr/2025 db/conf/iclr/iclr2025.html#Kasimbeg0EBSSFW25

Priya Kasimbeg Frank Schneider 0001 Runa Eschenhagen Juhan Bae Chandramouli Shama Sastry Mark Saroufim Boyuan Feng Less Wright Edward Z. Yang Zachary Nado Sourabh Medapati Philipp Hennig Michael Rabbat George E. Dahl Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition. 2025 February abs/2502.15015 CoRR https://doi.org/10.48550/arXiv.2502.15015 db/journals/corr/corr2502.html#abs-2502-15015 streams/journals/corr

Sourabh Medapati Priya Kasimbeg Shankar Krishnan Naman Agarwal George E. Dahl Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. 2025 March abs/2503.03986 CoRR https://doi.org/10.48550/arXiv.2503.03986 db/journals/corr/corr2503.html#abs-2503-03986 streams/journals/corr

Priya Kasimbeg Vincent Roulet Naman Agarwal Sourabh Medapati Fabian Pedregosa Atish Agarwala George E. Dahl How far away are truly hyperparameter-free learning algorithms? 2025 May abs/2505.24005 CoRR https://doi.org/10.48550/arXiv.2505.24005 db/journals/corr/corr2505.html#abs-2505-24005 streams/journals/corr

Zi Wang George E. Dahl Kevin Swersky Chansoo Lee Zachary Nado Justin Gilmer Jasper Snoek Zoubin Ghahramani Pre-trained Gaussian Processes for Bayesian Optimization. 212:1-212:83 2024 25 J. Mach. Learn. Res. https://jmlr.org/papers/v25/23-0269.html db/journals/jmlr/jmlr25.html#WangDSLNGSG24

George E. Dahl Frank Schneider 0001 Zachary Nado Naman Agarwal Chandramouli Shama Sastry Philipp Hennig Sourabh Medapati Runa Eschenhagen Priya Kasimbeg Daniel Suo Juhan Bae Justin Gilmer Abel L. Peirson Bilal Khan Rohan Anil Mike Rabbat Shankar Krishnan Daniel Snider Ehsan Amid Kongtao Chen Chris J. Maddison Rakshith Vasudev Michal Badura Ankush Garg Peter Mattson Benchmarking Neural Network Training Algorithms. 2023 abs/2306.07179 CoRR https://doi.org/10.48550/arXiv.2306.07179 db/journals/corr/corr2306.html#abs-2306-07179

Setareh Ariafar Justin Gilmer Zachary Nado Jasper Snoek Rodolphe Jenatton George E. Dahl Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. 11056-11071 2022 AISTATS https://proceedings.mlr.press/v151/ariafar22a.html conf/aistats/2022 db/conf/aistats/aistats2022.html#AriafarGNSJD22 Justin Gilmer Behrooz Ghorbani Ankush Garg Sneha Kudugunta Behnam Neyshabur David Cardoze George Edward Dahl Zachary Nado Orhan Firat A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. 2022 ICLR https://openreview.net/forum?id=OcKMT-36vUs conf/iclr/2022 db/conf/iclr/iclr2022.html#GilmerGGKNCDNF22

Ryan G. Gomes Bellington Vwalika Chace Lee Angelica Willis Marcin Sieniek Joan T. Price Christina Chen Margaret P. Kasaro James A. Taylor 0003 Elizabeth M. Stringer Scott Mayer McKinney Ntazana Sindano George E. Dahl William Goodnight III Justin Gilmer Benjamin H. Chi Charles Lau Terry Spitz T. Saensuksopa Kris Liu Jonny Wong Rory Pilgrim Akib Uddin Greg Corrado Lily Peng Katherine Chou Daniel Tse Jeffrey S. A. Stringer Shravya Shetty AI system for fetal ultrasound in low-resource settings. 2022 abs/2203.10139 CoRR https://doi.org/10.48550/arXiv.2203.10139 db/journals/corr/corr2203.html#abs-2203-10139

Zi Wang George E. Dahl Kevin Swersky Chansoo Lee Zelda Mariet Zachary Nado Justin Gilmer Jasper Snoek Zoubin Ghahramani Pre-training helps Bayesian optimization too. 2022 abs/2207.03084 CoRR https://doi.org/10.48550/arXiv.2207.03084 db/journals/corr/corr2207.html#abs-2207-03084

Jeremy Cohen 0001 Behrooz Ghorbani Shankar Krishnan Naman Agarwal Sourabh Medapati Michal Badura Daniel Suo David Cardoze Zachary Nado George E. Dahl Justin Gilmer Adaptive Gradient Methods at the Edge of Stability. 2022 abs/2207.14484 CoRR https://doi.org/10.48550/arXiv.2207.14484 db/journals/corr/corr2207.html#abs-2207-14484

Samuel R. Bowman George E. Dahl What Will it Take to Fix Benchmarking in Natural Language Understanding? 4843-4855 2021 NAACL-HLT https://doi.org/10.18653/v1/2021.naacl-main.385 https://aclanthology.org/2021.naacl-main.385/ conf/naacl/2021 db/conf/naacl/naacl2021.html#BowmanD21

Zachary Nado Justin Gilmer Christopher J. Shallue Rohan Anil George E. Dahl A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. 2021 abs/2102.06356 CoRR https://arxiv.org/abs/2102.06356 db/journals/corr/corr2102.html#abs-2102-06356

Samuel R. Bowman George E. Dahl What Will it Take to Fix Benchmarking in Natural Language Understanding? 2021 abs/2104.02145 CoRR https://arxiv.org/abs/2104.02145 db/journals/corr/corr2104.html#abs-2104-02145

Zi Wang George E. Dahl Kevin Swersky Chansoo Lee Zelda Mariet Zachary Nado Justin Gilmer Jasper Snoek Zoubin Ghahramani Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. 2021 abs/2109.08215 CoRR https://arxiv.org/abs/2109.08215 db/journals/corr/corr2109.html#abs-2109-08215

Justin Gilmer Behrooz Ghorbani Ankush Garg Sneha Kudugunta Behnam Neyshabur David Cardoze George E. Dahl Zachary Nado Orhan Firat A Loss Curvature Perspective on Training Instability in Deep Learning. 2021 abs/2110.04369 CoRR https://arxiv.org/abs/2110.04369 db/journals/corr/corr2110.html#abs-2110-04369

Christopher J. Shallue Jaehoon Lee 0001 Joseph M. Antognini Jascha Sohl-Dickstein Roy Frostig George E. Dahl Measuring the Effects of Data Parallelism on Neural Network Training. 112:1-112:49 2019 20 J. Mach. Learn. Res. https://jmlr.org/papers/v20/18-789.html db/journals/jmlr/jmlr20.html#ShallueLASFD19

Guodong Zhang 0006 Lala Li Zachary Nado James Martens Sushant Sachdeva George E. Dahl Christopher J. Shallue Roger B. Grosse Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. 8194-8205 2019 NeurIPS https://proceedings.neurips.cc/paper/2019/hash/e0eacd983971634327ae1819ea8b6214-Abstract.html http://papers.nips.cc/paper/9030-which-algorithmic-choices-matter-at-which-batch-sizes-insights-from-a-noisy-quadratic-model conf/nips/2019 db/conf/nips/nips2019.html#ZhangLNMSDSG19

Guodong Zhang 0006 Lala Li Zachary Nado James Martens Sushant Sachdeva George E. Dahl Christopher J. Shallue Roger B. Grosse Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. 2019 abs/1907.04164 CoRR http://arxiv.org/abs/1907.04164 db/journals/corr/corr1907.html#abs-1907-04164

Dami Choi Alexandre Passos Christopher J. Shallue George E. Dahl Faster Neural Network Training with Data Echoing. 2019 abs/1907.05550 CoRR http://arxiv.org/abs/1907.05550 db/journals/corr/corr1907.html#abs-1907-05550

Dami Choi Christopher J. Shallue Zachary Nado Jaehoon Lee 0001 Chris J. Maddison George E. Dahl On Empirical Comparisons of Optimizers for Deep Learning. 2019 abs/1910.05446 CoRR http://arxiv.org/abs/1910.05446 db/journals/corr/corr1910.html#abs-1910-05446

Nicolas Ford Daniel Duckworth Mohammad Norouzi 0002 George E. Dahl The Importance of Generation Order in Language Modeling. 2942-2946 2018 EMNLP https://doi.org/10.18653/v1/d18-1324 https://aclanthology.org/D18-1324/ conf/emnlp/2018 db/conf/emnlp/emnlp2018.html#FordD0D18 Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton Large scale distributed neural network training through online distillation. 2018 ICLR (Poster) https://openreview.net/forum?id=rkr1UDeC- conf/iclr/2018 db/conf/iclr/iclr2018.html#AnilPPODH18 Bhuwan Dhingra Christopher J. Shallue Mohammad Norouzi 0002 Andrew M. Dai George E. Dahl Embedding Text in Hyperbolic Spaces. 59-69 2018 TextGraphs@NAACL-HLT https://doi.org/10.18653/v1/w18-1708 https://aclanthology.org/W18-1708/ conf/textgraphs/2018 db/conf/textgraphs/textgraphs2018.html#DhingraSNDD18

Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton Large scale distributed neural network training through online distillation. 2018 abs/1804.03235 CoRR http://arxiv.org/abs/1804.03235 db/journals/corr/corr1804.html#abs-1804-03235

Manoj Kumar 0019 George E. Dahl Vijay Vasudevan Mohammad Norouzi 0002 Parallel Architecture and Hyperparameter Search via Successive Halving and Classification. 2018 abs/1805.10255 CoRR http://arxiv.org/abs/1805.10255 db/journals/corr/corr1805.html#abs-1805-10255

Peter W. Battaglia Jessica B. Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinícius Flores Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner 0001 Çaglar Gülçehre H. Francis Song Andrew J. Ballard Justin Gilmer George E. Dahl Ashish Vaswani Kelsey R. Allen Charles Nash Victoria Langston Chris Dyer Nicolas Heess Daan Wierstra Pushmeet Kohli Matthew M. Botvinick Oriol Vinyals Yujia Li 0001 Razvan Pascanu Relational inductive biases, deep learning, and graph networks. 2018 abs/1806.01261 CoRR http://arxiv.org/abs/1806.01261 db/journals/corr/corr1806.html#abs-1806-01261

Bhuwan Dhingra Christopher J. Shallue Mohammad Norouzi 0002 Andrew M. Dai George E. Dahl Embedding Text in Hyperbolic Spaces. 2018 abs/1806.04313 CoRR http://arxiv.org/abs/1806.04313 db/journals/corr/corr1806.html#abs-1806-04313

Justin Gilmer Ryan P. Adams Ian J. Goodfellow David G. Andersen George E. Dahl Motivating the Rules of the Game for Adversarial Example Research. 2018 abs/1807.06732 CoRR http://arxiv.org/abs/1807.06732 db/journals/corr/corr1807.html#abs-1807-06732

Nicolas Ford Daniel Duckworth Mohammad Norouzi 0002 George E. Dahl The Importance of Generation Order in Language Modeling. 2018 abs/1808.07910 CoRR http://arxiv.org/abs/1808.07910 db/journals/corr/corr1808.html#abs-1808-07910

Christopher J. Shallue Jaehoon Lee 0001 Joseph M. Antognini Jascha Sohl-Dickstein Roy Frostig George E. Dahl Measuring the Effects of Data Parallelism on Neural Network Training. 2018 abs/1811.03600 CoRR http://arxiv.org/abs/1811.03600 db/journals/corr/corr1811.html#abs-1811-03600

Justin Gilmer Samuel S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl Neural Message Passing for Quantum Chemistry. 1263-1272 2017 ICML http://proceedings.mlr.press/v70/gilmer17a.html conf/icml/2017 db/conf/icml/icml2017.html#GilmerSRVD17

Yun Liu 0013 Krishna Gadepalli Mohammad Norouzi 0002 George E. Dahl Timo Kohlberger Aleksey Boyko Subhashini Venugopalan Aleksei Timofeev Philip Q. Nelson Gregory S. Corrado Jason D. Hipp Lily Peng Martin C. Stumpe Detecting Cancer Metastases on Gigapixel Pathology Images. 2017 abs/1703.02442 CoRR http://arxiv.org/abs/1703.02442 db/journals/corr/corr1703.html#LiuGNDKBVTNCHPS17

Justin Gilmer Samuel S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl Neural Message Passing for Quantum Chemistry. 2017 abs/1704.01212 CoRR http://arxiv.org/abs/1704.01212 db/journals/corr/corr1704.html#GilmerSRVD17

George E. Dahl Deep Learning Approaches to Problems in Speech Recognition, Computational Chemistry, and Natural Language Text Processing. University of Toronto, Canada 2015 http://hdl.handle.net/1807/69304

Junshui Ma Robert P. Sheridan Andy Liaw George E. Dahl Vladimir Svetnik Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. 263-274 2015 55 J. Chem. Inf. Model. 2 https://doi.org/10.1021/ci500747n https://www.wikidata.org/entity/Q41506565 db/journals/jcisd/jcisd55.html#MaSLDS15

Tara N. Sainath Brian Kingsbury George Saon Hagen Soltau Abdel-rahman Mohamed George E. Dahl Bhuvana Ramabhadran Deep Convolutional Neural Networks for Large-scale Speech Tasks. 39-48 2015 64 Neural Networks https://doi.org/10.1016/j.neunet.2014.08.005 https://www.wikidata.org/entity/Q50620670 db/journals/nn/nn64.html#SainathKSSMDR15

George E. Dahl Navdeep Jaitly Ruslan Salakhutdinov Multi-task Neural Networks for QSAR Predictions. 2014 abs/1406.1231 CoRR http://arxiv.org/abs/1406.1231 db/journals/corr/corr1406.html#DahlJS14

Ryan Prescott Adams George E. Dahl Iain Murray 0001 Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. 2014 abs/1408.2039 CoRR http://arxiv.org/abs/1408.2039 db/journals/corr/corr1408.html#AdamsDM14

Tara N. Sainath Brian Kingsbury Abdel-rahman Mohamed George E. Dahl George Saon Hagen Soltau Tomás Beran Aleksandr Y. Aravkin Bhuvana Ramabhadran Improvements to Deep Convolutional Neural Networks for LVCSR. 315-320 2013 ASRU https://doi.org/10.1109/ASRU.2013.6707749 conf/asru/2013 db/conf/asru/asru2013.html#SainathKMDSSBAR13 George E. Dahl Jack W. Stokes Li Deng 0001 Dong Yu 0001 Large-scale malware classification using random projections and neural networks. 3422-3426 2013 ICASSP https://doi.org/10.1109/ICASSP.2013.6638293 conf/icassp/2013 db/conf/icassp/icassp2013.html#DahlSDY13 George E. Dahl Tara N. Sainath Geoffrey E. Hinton Improving deep neural networks for LVCSR using rectified linear units and dropout. 8609-8613 2013 ICASSP https://doi.org/10.1109/ICASSP.2013.6639346 conf/icassp/2013 db/conf/icassp/icassp2013.html#DahlSH13 Ilya Sutskever James Martens George E. Dahl Geoffrey E. Hinton On the importance of initialization and momentum in deep learning. 1139-1147 2013 ICML (3) http://proceedings.mlr.press/v28/sutskever13.html conf/icml/2013 db/conf/icml/icml2013.html#SutskeverMDH13

Tara N. Sainath Brian Kingsbury Abdel-rahman Mohamed George E. Dahl George Saon Hagen Soltau Tomás Beran Aleksandr Y. Aravkin Bhuvana Ramabhadran Improvements to deep convolutional neural networks for LVCSR. 2013 CoRR http://arxiv.org/abs/1309.1501 abs/1309.1501 db/journals/corr/corr1309.html#SainathKMDSSBAR13

Abdel-rahman Mohamed George E. Dahl Geoffrey E. Hinton Acoustic Modeling Using Deep Belief Networks. 14-22 2012 20 IEEE Trans. Speech Audio Process. 1 https://doi.org/10.1109/TASL.2011.2109382 https://www.wikidata.org/entity/Q56058041 db/journals/taslp/taslp20.html#MohamedDH12

George E. Dahl Dong Yu 0001 Li Deng 0001 Alex Acero Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. 30-42 2012 20 IEEE Trans. Speech Audio Process. 1 https://doi.org/10.1109/TASL.2011.2134090 https://www.wikidata.org/entity/Q55897737 db/journals/taslp/taslp20.html#DahlYDA12

George E. Dahl Ryan Prescott Adams Hugo Larochelle Training Restricted Boltzmann Machines on Word Observations. 2012 ICML http://icml.cc/2012/papers/364.pdf conf/icml/2012 db/conf/icml/icml2012.html#DahlAL12

George E. Dahl Ryan Prescott Adams Hugo Larochelle Training Restricted Boltzmann Machines on Word Observations http://arxiv.org/abs/1202.5695 2012 CoRR abs/1202.5695 db/journals/corr/corr1202.html#abs-1202-5695

George E. Dahl Dong Yu 0001 Li Deng 0001 Alex Acero Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. 4688-4691 2011 ICASSP https://doi.org/10.1109/ICASSP.2011.5947401 conf/icassp/2011 db/conf/icassp/icassp2011.html#DahlYDA11 Abdel-rahman Mohamed Tara N. Sainath George E. Dahl Bhuvana Ramabhadran Geoffrey E. Hinton Michael A. Picheny Deep Belief Networks using discriminative features for phone recognition. 5060-5063 2011 ICASSP https://doi.org/10.1109/ICASSP.2011.5947494 conf/icassp/2011 db/conf/icassp/icassp2011.html#MohamedSDRHP11 George E. Dahl Marc'Aurelio Ranzato Abdel-rahman Mohamed Geoffrey E. Hinton Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. 469-477 2010 NIPS https://proceedings.neurips.cc/paper/2010/hash/b73ce398c39f506af761d2277d853a92-Abstract.html http://papers.nips.cc/paper/4169-phone-recognition-with-the-mean-covariance-restricted-boltzmann-machine conf/nips/2010 db/conf/nips/nips2010.html#DahlRMH10 Ryan Prescott Adams George E. Dahl Iain Murray 0001 Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. 1-9 2010 UAI https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2085&proceeding_id=26 conf/uai/2010 db/conf/uai/uai2010.html#AdamsDM10

Ryan Prescott Adams George E. Dahl Iain Murray 0001 Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes http://arxiv.org/abs/1003.4944 2010 CoRR abs/1003.4944 db/journals/corr/corr1003.html#abs-1003-4944

Alex Acero Ryan P. AdamsRyan Prescott Adams Naman Agarwal Atish Agarwala Kelsey R. Allen Ehsan Amid David G. Andersen Rohan Anil Joseph M. Antognini Aleksandr Y. Aravkin Setareh Ariafar Michal Badura Juhan Bae Andrew J. Ballard Victor Bapst Peter W. Battaglia Tomás Beran Matt M. BotvinickMatthew M. Botvinick Samuel R. Bowman Aleksey Boyko David Cardoze Christina Chen Kongtao Chen Benjamin H. Chi Dami Choi Katherine Chou Jeremy Cohen 0001 Gregory S. CorradoGreg Corrado Andrew M. Dai Li Deng 0001 Bhuwan Dhingra Daniel Duckworth Chris Dyer Runa Eschenhagen Ryan Faulkner 0001 Boyuan Feng Orhan Firat Nicolas Ford Roy Frostig Krishna Gadepalli Ankush Garg Zoubin Ghahramani Behrooz Ghorbani Justin Gilmer Ryan G. Gomes Ian J. Goodfellow William Goodnight III Roger B. Grosse Caglar GulcehreÇaglar Gülçehre Jessica B. Hamrick Nicolas Heess Philipp Hennig Geoffrey E. Hinton Jason D. Hipp Navdeep Jaitly Rodolphe Jenatton Margaret P. Kasaro Priya Kasimbeg Bilal Khan Brian Kingsbury Timo Kohlberger Pushmeet Kohli Shankar Krishnan Sneha Reddy KuduguntaSneha Kudugunta Manoj Kumar 0019 Victoria Langston Hugo Larochelle Charles Lau Chace Lee Chansoo Lee Jaehoon Lee 0001 Lala Li Yujia Li 0001 Andy Liaw Kris Liu Yun Liu 0013 Junshui Ma Chris J. Maddison Mateusz Malinowski Zelda Mariet James Martens Peter Mattson Scott Mayer McKinney Sourabh Medapati Abdel-rahman Mohamed Iain Murray 0001 Zachary Nado Charles Nash Philip Q. Nelson Behnam Neyshabur Mohammad Norouzi 0002 Róbert Ormándi Razvan Pascanu Alexandre Tachard PassosAlexandre Passos Fabian Pedregosa Abel L. Peirson Lily Peng Gabriel Pereyra Michael PichenyMichael A. Picheny Rory Pilgrim Joan T. Price Michael G. RabbatMichael RabbatMike Rabbat Bhuvana Ramabhadran Marc'Aurelio Ranzato David Raposo Patrick F. Riley Vincent Roulet Sushant Sachdeva T. Saensuksopa Tara N. Sainath Ruslan Salakhutdinov Alvaro Sanchez-Gonzalez Adam Santoro George Saon Mark Saroufim Chandramouli Shama Sastry Frank Schneider 0001 Samuel S. Schoenholz Christopher J. Shallue Robert P. Sheridan Shravya Shetty Marcin Sieniek Ntazana Sindano Daniel Snider Jasper Snoek Jascha Sohl-Dickstein Hagen Soltau H. Francis Song Terry Spitz Jack W. Stokes Elizabeth M. Stringer Jeffrey S. A. Stringer Martin C. Stumpe Daniel Suo Ilya Sutskever Vladimir Svetnik Kevin Swersky Andrea Tacchetti James A. Taylor 0003 Aleksei Timofeev Daniel Tse Akib Uddin Rakshith Vasudev Vijay Vasudevan Ashish Vaswani Subhashini Venugopalan Oriol Vinyals Bellington Vwalika Zi Wang Daan Wierstra Angelica Willis Jonny Wong Less Wright Edward Z. Yang Dong Yu 0001 Vinícius Flores Zambaldi Guodong Zhang 0006